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The world becomes advance rapidly, and the demand of perishable food increases in the global market. Food firms perceive the cheapest supply chain process for the delivery of products to end consumers. Apart from that, consumer demands high quality and safe products with competitive price. In the global intense competition, China introduced One Belt One Road (OBOR) advanced feature supply chain management system to cut the cost of production for these product firms as well as functionally deliver high quality and green products to end consumer without affecting from high environment temperature. Respectively, the graphical research model and the multi-objective method were developed to examine the estimated perishable product trading figures with consideration to quality, which is achieved by accumulating the advanced transportation features offered by OBOR supply chain management as compared to ancient rail route supply chain. To prove this, "simulation optimization function was applied to measure the probability of time-saving for perishable product quality from environmental effects and its influences over product demand." Some perishable products were selected, and their trading figures and demand value were measured by comparing both rail route environmental effects over demand, weights of products, and trade. The results declare perishable food quality and trading volume increased due to fast delivery of products to numerous countries, having cold supply chain feature under OBOR supply chain management and estimated 25 days of time-saving. Comparative analysis discloses the coherent picture of both trading routes used for delivering the products. "The findings show large amount of time-saving maximizes perishable product quality from environmental influence" estimated 3 times higher with fast train supply chain. Conceptually, perceived from the idea, when and if maglev train 600 km over an hour will be used for perishable product supply chain purpose in the near future, the estimated quality of perishable products and trading is considered to be more than 5 times higher as compared to ancient supply chain route. The study suggests future research direction on topics, food quality along with supply chain management system, and environmental impact measurement policy under different supply chain routes.

Background The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots. Objective The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis. Methods We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence. Results The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention. Conclusions Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stakeholders about AI technology; and (4) value-based health care including “allocative value,” “technology value,” and “personalized value.”

More and more manufacturers are now engaging in online sales, but the online sales of time-sensitive commodities still faces severe challenges because of short delivery lead time and high home-delivery costs. To reduce the home-delivery costs, manufacturers with online retailing operations coordinate with offline retailers by adopting a "preorder online, home-delivery offline" (POHO) strategy. To effectively implement the POHO strategy, a centralized coordination mechanism is introduced. Specially, a profit-sharing contract between manufacturer and retailer is designed under the centralized scenario. By comparing the optimal profits before and after the implementation of the coordination strategy, we obtain the feasibility conditions for the online-and-offline coordination between the manufacturer and the retailer. We find that the POHO strategy can reduce the retail prices in both channels, and conclude that it is a viable solution to both retailers and customers involved.

BACKGROUND The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots. OBJECTIVE The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis. METHODS We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence. RESULTS The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention. CONCLUSIONS Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stakeholders about AI technology; and (4) value-based health care including “allocative value,” “technology value,” and “personalized value.”

We investigate a fresh food supply chain comprising a large-scale supplier and multiple small-scale retailers under a carbon cap-and-trade policy. Retailers' joint replenishment and the carbon trading behavior of supply chain members are studied. We assume that three replenishment strategies are available for the supply chain: (1) separate replenishment; (2) joint replenishment: a leader-follower relationship among retailers; and (3) joint replenishment: the coalition of retailers. Under each strategy, a bargaining framework for supply chain members is set up to maximize their profits, where the price of the refrigerated transportation services provided by the supplier and the retail price of fresh food are optimized. The optimal decisions are analyzed to provide insights into logistics pricing and retail pricing strategies. Through comparing three replenishment strategies, we also identify the optimal replenishment strategies from the perspectives of the supplier, retailers and a carbon emission optimizer. Moreover, we investigate the role of the carbon cap-and-trade policy by comparing the cases with and without a carbon cap-and-trade policy. It is noteworthy that the goals of profit growth and emission reduction are simultaneously achieved under the carbon cap-and-trade policy.

In this research, we address the issue how the supermarket properly choose and balance the preservation technology (PT) investment and the pricing of fresh food in an imperfectly competitive market environment, in which the shortage is allowed during a sales period. If the inventory level decreases only by the demand, we have proved that there exists an optimal pair of PT investment and pricing of fresh food to maximize the total profit during a cycle. Proper PT investment and pricing can bring more profits but the supermarket should investigate the market type and adopt different strategies. In the extended model, the inventory level in a sales cycle drops to zero due to demand and deterioration owing to the nature of perishable foods, and the reduced inventory loss will be affected by PT investment. When the initial deterioration rate is high, it means that the supermarket should invest in PT. Otherwise, the optimal strategy for the supermarket is not to invest in PT.

Objective The aim of this study is to investigate the psychological determinants on elderly’ acceptance of general practitioners. Methods This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) model by considering the aspect of trust, perceived utility, and satisfaction, and compares it with other psychological determinants. A questionnaire survey was conducted in seven cities in central China from November 2017 to March 2018, Changsha, Nanjing, Wuhan, Nanchang, Guangzhou, Zhengzhou and Hefei, and 646 valid samples (> = 60 years old) were collected. Results All the structures met the requirements and the discriminant validity of the data was acceptable. Eleven hypotheses were supported at the significance level of 0.05. Performance expectancy, effort expectancy, social influence, and facilitating conditions were considered positive determinants of behavioral intention; trust was considered as a positive determinant of behavioral intention and adoption behavior; behavioral intention was considered as positive determinant of adoption behavior; adoption behavior was considered positive determinants of both satisfaction and perceived utility; perceived utility was positive determinant of satisfaction, and satisfaction positive influence behavioral intention. Conclusions It is necessary to integrate trust, satisfaction, and perceived utility into the extended UTAUT model, which would have good explanatory power on elderly’s behavioral intention for adopting general practitioners. These understandings can prompt policymakers to better understand the psychological perception and behavioral intention of elderly.

Background and purpose Telehealth bring significant benefits including improved quality of healthcare, efficiency and cost containment, especially for chronic patients and the elderly. China is the second largest country of investment in telemedicine systems, but the acceptance and behavioral intentions of the technology are still low in the elderly. The objective of this study is to explain the micro-mechanism that determines the behavioral intentions to use telemedicine systems from the perspective of elderly patients based on an extended Technology Acceptance Model. Methods A sample consisting of 436 elderlies selected through multistage cluster sampling from four cities in mainland China. The empirical study was conducted to examine the proposed model by two aspects: measurement model and structural model. Results The study determined that medical service satisfaction (t = 6.770, β = 0.332), ease of use (t = 5.200, β = 0.179), information quality (t = 12.540, β = 0.639) had a significant impact on the elderly patients’ acceptance to telehealth, and the acceptance had a significant impact (t = 14.356, β = 0.697) on the elderly patients’ behavior intentions of telehealth. The results also show that the variable of acceptance has significant mediating effects among the theoretical model. Conclusions This study confirms the applicability of the extended Technology Acceptance Model in the behavioral intentions among elderly people in China using telehealth systems. The results indicate that relationship between telehealth systems and physical medical services are mutually reinforcing rather than alternative. The study will help technology developers better understand the behavioral characteristics of the elderly and encourage healthcare providers to better understand the true need of telehealth systems. These findings provide valuable information to telehealth system developers, governments, investors, and hospitals to promote the use of this technology by elderly patients.

Health Data Bank (HDB) as a type of health data service institution has become a promising third-party for mining the value behind the huge amounts of health data and providing valuable health data service to ordinary people. The customers’ continuous contribution behavior is the key element of HDB’s success and sustainable development. In this paper, we consider creditworthiness value as a measurement of a customer’s contribution behavior. The customers’ creditworthiness value is computed based on both the Authentic Behavior and Honest Behavior. Then, we propose a scheme Creditworthiness-based Service Differentiation (CBSD), in which the creditworthiness value is used as the guideline for differential service. The proposed scheme provides the right incentives for the customers to share their valid and authentic personal health data. And the simulation results confirm the ability of the proposed scheme to effectively reduce the health data service level provided to the customers with low creditworthiness value. On the other hand, good customers have a high probability of receiving better service. The shortcomings of the proposed scheme and the future work are concluded at the end of the paper.

Spatially specific intervention is proven to be more cost-effective than the “one-fit-all” strategy in controlling infectious disease outbreaks. However, it presents decision challenges due to partially observable epidemic state information and imperfectly determined model parameters. With deployment of mobile sensor, additional information such as geo-tagged bacteria concentration can be acquired, which can help to improve the understanding of the epidemic state and model parameters. Thus, a mobile sensor dispatching problem deciding further sensing spots should be studied considering the surveillance capacity. To solve this problem, we develop a metapopulation model based predictive control approach for making intelligent public health intervention decisions and sensor spatial dispatch decisions for cholera outbreak control. This approach incorporates a mobile sensor deployment scheme considering the improvement on unmeasurable parameter estimation and epidemic state prediction. Then it optimizes spatially specific intervention strengths to mitigate the harm of the infected population with progressively gained understanding of the system via mobile sensing.

With behavior-based pricing (BBP), firms use consumers’ purchase history data to price discriminate between new and past consumers. In this paper, we investigate BBP in a competitive setting, which consists of a standard product firm and a custom product firm. We examine how firms’ adoption of BBP affects product variety, lead time, and price decisions. We formulate this problem as a two-period game model. Through the analysis of equilibrium outcomes, we find that BBP by the standard product firm alone decreases product variety, whereas BBP by the custom product firm alone increases lead time. In addition, BBP increases one firm’s market share when only this firm uses BBP. Furthermore, the impact of BBP on profits is influenced by the consumer sensitivity to lead time and the cost incurred by reducing lead time.

In some real-world applications, such as law enforcement, it is required to find out the true identity of an LR face image by using only the high-resolution (HR) profile face image. This leads to LR face recognition with single sample per person (SSPP). As it is intractable to find a match between LR and HR images directly and difficult to explore variations from SSPP, LR face recognition with SSPP is quite a challenging problem. To address the problem, based on simultaneously discriminative analysis (SDA), we propose to introduce an auxiliary dataset (source domain) containing multiple samples per person and integrate domain adaptation into the learning of coupled mappings. In the proposed method, the discriminative analysis of source domain and target domain, together with the domain adaptation between the source domain and target domain are unified into one framework, which produces Enhanced SDA. In this framework, the distribution mismatch is reduced, and the information in both the source domain and target domain are used to learn the coupled mappings. Enhanced SDA is extensively evaluated on LFW and SC face databases, the promising results of Enhanced SDA show its effectiveness in LR face recognition with single sample per person.

Background: The huge gap in adolescent human papillomavirus (HPV) uptake between China ( 60%) indicates the necessity of comprehensive examination on the modifiable factors for parents’ decision-making. The present study investigated parental perceptions of HPV vaccination for children in China from a socio-economic perspective. Methods: Parents of 9-to-14-year-old adolescents who understood the HPV vaccination (n = 925) completed our questionnaire survey in Fall 2017. Based on the structural equation modeling, we examined the relationship among knowledge, awareness, and intentions of 20 items by 7-point Likert scale, the moderating effects also were tested among 5 socioeconomic variables. Results: Parents of female students have more positive intention about the HPV vaccination than male students’ parents (3.74 vs 2.80, p

The increasing amount of carbon emissions has caused global warming and challenged the sustainable development of environment. Governments around the world have implemented carbon policies including carbon cap-and-trade policy. In this paper, we focus on how a two-echelon supply chain manages its carbon footprints in production and inventory under carbon cap-and-trade policy. We extend the classical EOQ (economic order quantity) model and study decisions on production-inventory, carbon trading and emission reduction investment in the decentralized and centralized situations. The results show that emission permit sharing can effectively reduce the total cost and total carbon emissions of the supply chain. Moreover, the manufacturer’s emission reduction effort rises with the increase of the buying and selling prices of emission permits under centralized decision-making. In addition, a compensation mechanism is proposed for the centralized supply chain with emission permit sharing. It is observed that the buying and selling prices of emission permits have a positive influence on the permit sharing price in the compensation mechanism. Meanwhile, the retailer pays less for using the emission permits if it has a higher carbon cap, while the manufacturer with a higher carbon cap is more capable to provide a high compensation for the retailer.

Parental behavioral intentions play a key role in children vaccination. The present study looks at how parental expectations for the vaccination influence their attitudes and behavioral intentions toward children's immunization. The theoretical model is developed on TPB model and empirically tested with sample data from 380 children aged 0-7 years in mainland China. The structural equation modeling and multi-group analysis were used in the model to demonstrate the impact of the vaccination services quality, vaccination promotions and public opinions on attitudes and vaccination behavioral intention. Results support the study's hypotheses and indicate that Vaccination Services Quality (VSQ), Vaccination Promotions (VAP) and Public Opinions (PUO) have positive effects on Attitudes (ATT) and Vaccination Behavioral Intention (VAI), VSQ has the strongest standardized path (.594) while PUO has the least influence (.131), and Attitude is complete mediation in all paths. Attitude is key factor that determine the parental behavioral intentions, and the internal perception comes from the vaccination services quality and the external influence comes from the social environment affect the parental attitude. Governments should implement effective policies and improve vaccination services quantity to eliminate parental misunderstandings.

Carbon cap-and-trade regulation is widely adopted to reduce carbon emissions. Under this regulation, we propose a carbon trading mechanism considering refrigerated logistics services in a fresh foo...

Collecting samples is one of the main difficulties for face recognition, for example, in most of the real-world applications such as law enhancement, e-passport, and ID card identification, it is customary to collect a single sample per person (SSPP). Unfortunately, in such SSPP scenario, many presented face recognition methods suffer serious performance drop or fail due to their inability to learn the discriminative information of a person from a single sample. To address the SSPP problem, in this paper, we propose a multiple feature subspaces analysis (MFSA) approach, which takes advantage of facial symmetry. First, we divide each enrolled face into two halves about the bilateral symmetry axis and further partition every half into several local face patches. Second, we cluster all the patches into multiple groups according to their locations at the half face and formulate SSPP as a MFSA problem by learning a feature subspace for each group, so that the confusion between inter-class and intra-class variations of face patches is removed and more discriminative features can be extracted from each subspace. To recognize a target person, a k-NN classifier is employed in each subspace to predict the label of a face patch and majority voting strategy is used to identify the unlabeled subject. Compared with the state-of-the-art methods, MFSA is effortless and efficient in implementing, but achieves either better or competitive performance when recognizing face images taken in both constrained and unconstrained environment.

Significant uncertainty associated with Chinese urban logistics, caused by random vehicle operational restrictions due to severe weather (e.g., smog) in addiction to normal traffic variation makes the tactical production and distribution planning decisions quite challenging. In this paper, we propose a two-stage stochastic integer programming model for an optimal production distribution capacity planning problem under the aforementioned uncertainties. We aim to minimize both procurement spending and the expected operational cost under logistic uncertainty. Given the computational burden of solving the resultant stochastic integer program for real-world instances, we develop an improved stochastic branch-and-bound (SBB) algorithm embedding with Tabu search method. We conduct the numerical study to verify the superiority of the proposed algorithm. We also offer managerial insights to practitioners and policy recommendations to municipal governments based on the numerical study results.

In a two-stage medical service supply chain system composed of health data bank and medical knowledge provider, the willingness of medical knowledge providers to share medical knowledge is of great importance. Based on the principal-agent theory, this paper designs an incentive mechanism to promote medical knowledge providers’ efforts to improve the storage of high-quality medical knowledge based on the double incentive dimension of “remuneration + value”, and comprehensively analyzes the relationship between the optimal effort level and the incentive intensities. The study finds that the level of effort of medical knowledge providers is not only affected by the incentive intensities, but also affected by their own conditions such as professional proficiency. Remuneration incentives can always play a positive role, and there is a mutual substitution relationship between the two remuneration incentive intensities of health data bank and medical institution. Value incentive may play a counter role, and the setting of value incentive intensity needs to be adjusted according to the status of the medical knowledge providers themselves.

As a growing number of low-resolution (LR) face images are captured by surveillance cameras, LR face recognition has been a hot issue for recent years. Previous efforts on LR face recognition typically assume each subject has multiple high-resolution (HR) training samples. However, this assumption may not hold in some special cases such as law-enforcement where only a single HR sample per person exists in the training set. For LR face recognition in SSPP scenario, it often suffers from overfitting and singular matrix problems. In this paper, we are the first to investigate LR face recognition with single sample per person, and propose a cluster-based regularized simultaneous discriminant analysis (C-RSDA) method based on SDA. C-RSDA regularizes the between-class and within-class scatter matrices respectively with inter-cluster and intra-cluster scatter matrices, where the cluster-based scatter matrices are computed from unsupervised clustering. With the cluster-based scatter matrices, not only the singularity problem is resolved, but overfitting problem is overcomed as more variations are exploited from the limited training samples. Thus, the proposed C-RSDA enhances the discriminative power of the feature subspace. We extensively evaluate C-RSDA on recognizing LR face images captured in both controlled and uncontrolled environments. The encouraging experimental results demonstrate the effectiveness of the proposed approach.

Drawing upon the social functionalist view of emotions, we tested expresser- and object-informational processes that transmitted the effect of leader appreciation expression onto employee job perfo...

Bifurcation and control of fractional-order systems are still an outstanding problem. In this paper, a novel delayed fractional-order model of small-world networks is introduced and several topics related to the dynamics and control of such a network are investigated, such as the stability, Hopf bifurcations, and bifurcation control. The nonlinear interactive strength is chosen as the bifurcation parameter to analyze the impact of the interactive strength parameter on the dynamics of the fractional-order small-world network model. Firstly, the stability domain of the equilibrium is completely characterized with respect to network parameters, delays and orders, and some explicit conditions for the existence of Hopf bifurcations are established for the delayed fractional-order model. Then, a fractional-order Proportional-Derivative (PD) feedback controller is first put forward to successfully control the Hopf bifurcation which inherently happens due to the change of the interactive parameter. It is demonstrated that the onset of Hopf bifurcations can be delayed or advanced via the proposed fractional-order PD controller by setting proper control parameters. Meanwhile, the conditions of the stability and Hopf bifurcations are obtained for the controlled fractional-order small-world network model. Finally, illustrative examples are provided to justify the validity of the control strategy in controlling the Hopf bifurcation generated from the delayed fractional-order small-world network model.

Objective To identify the changing of health policy decisions and Doctor–Patient relationship in different periods in China during 67 years (1949–2016). Methods We followed a systematic approach to analyze the development stage of China׳s healthcare reform and Doctor–Patient relationship over time. Based on the extant literature and statistical data, the current five issues in Chinese health policy and three aspects of Doctor-Patient Relationships are classified. The reasons for the current situation and recommendations for future policy are discussed. Results There are four main problems of Chinese Health Policy: funding shortage, excessive market-oriented operation, health insurance limited reimbursement amount, and media coverage a lot of non-neutral health events. In different historical periods, these problems are not prominent, but have become increasingly serious in recent years. On the other hand, three participants contributed to the growing tense relationship between doctors and patients: doctors (hospitals), patients and governments. Conclusions In formulating future health care policy, the government should change: increase investment in the medical field, change the income structure of doctors and hospital, propagate medical news in an objective way, and increase the amount and scope of medical insurance repayment.

Linear discriminant analysis (LDA) is one of the most popular feature extraction methods in image recognition. Nevertheless, LDA always suffers from some undesired behaviors caused by globality, namely ignoring local geometric structure of images. To fully capture the geometric information, we propose a novel two-dimensional linear discriminant analysis with locality preserving (2DLP-LDA) approach, which can cover both within-class and between-class local geometric information. 2DLP-LDA re-characterizes the within-class scatter matrix by employing locality preserving projection technique and re-characterizes the between-class scatter matrix by introducing a Gaussian weighting function, making the samples from the same class moderately cluster, while the samples from different classes properly distribute in the reduced subspace. Encouraging experimental results on FERET face database as well as SEU bovine iris database show the effectiveness of the proposed approach.

To cope with the supply-demand mismatch in the global market, transnational enterprises usually adopt the strategy of lateral transshipment. This paper studies the production planning, transshipment and carbon trading problem of a large transnational manufacturer in a global supply chain. Two countries are involved in the operation of the transnational manufacturer, where different carbon trading mechanisms are implemented. Operational decisions of the transnational manufacturer are optimized in a mixed integer programming model with the purpose of maximizing its total profit. Then, we analyze the influence of the difference between carbon trading mechanisms on the transnational manufacturer, retailers, the countries involved and the global supply chain. Theoretically, our research enriches the research streams of lateral transshipment and carbon cap-and-trade policy. Moreover, managerial insights are provided for global supply chain members and international emission reduction regulators, which contribute to the smooth operation of global supply chains and enhance emission reduction. Especially, the conditions under which local governments should emphasize on economic development or environmental protection are revealed.

We consider the cost allocation problems for the cooperative inventory consolidation programs under the time-based policy and under the quantity-based policy. We investigate the concavity of the cooperative games and propose a proportional cost allocation rule for each policy. We analyze the population monotonicity and stability of the rule. We conduct theoretical and numerical analysis to study the cooperation stability and individual relative cost savings.

In this paper, a supply chain with a manufacturer and an Internet sharing platform who leases products to customers is considered. We solve the optimization problem in two structures, namely, without and with recycling. In basic structure, manufacturer produces new products and sells them to the platform. Then platform leases products to customers with per-use price and service level. Since platform keeps the ownership of products, reverse logistics from platform to manufacturer is explored. In both cases, cooperative and noncooperative relationships between manufacturer and platform are discussed. The results show cooperation is better than non-cooperation in profit and service aspects. Total profit in non-cooperative case accounts for 3/4 of the total profit in cooperative case, among which manufacturer’s profit is 1/2 and platform’s profit is 1/4. We also investigate the coordination policy between manufacturer and platform in noncooperative case via the recycling subsidy and find that subsidy affects the wholesale price merely. An example is given to illustrate values and sensitivity analyses are performed theoretically and numerically.

It has been well-established that some leader traits and behaviors can have detrimental effects on individual followers, teams, and organizations (Einarsen, Aasland, & Skogstad, 2007; Krasikova, Gr...

Self-service parcel delivery service has been favored by E-commerce retailers as an effective solution to the “last-mile” delivery, and consumers' adoption behavior is the key point to apply this emerging technology. The aim of the present study is to empirically test the influence of psychological factors on online consumers’ behavioral intention to adopt it. An extended UTAUT model is developed and 525 structured questionnaires were collected. Empirical results demonstrate that performance expectancy, effort expectancy, social influence and facilitating conditions are positive determinants, while perceived risk was negatively factor to behavioral intention. The difference in the behavioral intention among demographic groups was examined as it might provide an opportunity for developing a new strategy to promote the application of self-service parcel delivery service for last-mile delivery.

Background The public acceptance and implementation of vaccination programs is essential to prevent infectious diseases. However, vaccine adverse events may cause public panic and eventually lead to an increasing number of populations who were hesitant or refuse to participate in these vaccination programs. In 2018, the Changsheng vaccine crisis broke out in mainland China, and 252,600 unqualified DTP vaccines were reported to be used for child vaccination. In this study, we observed media and public reactions toward the vaccine crisis. Methods This study conducted Internet surveillance by four mainstream indicators from July 15th to August 7th, including social media (WeChat, Sina Weibo), online news and Baidu search index. We also analyzed the emotional perceptions of people in crisis through an online questionnaire survey. Results During the crisis, huge number of articles emerged on Internet, 125,882,894 articles (including forwarding) on WeChat friends circle, 1,877,660 Sina Weibo posts, 648,265 online news and 4,986,521 Baidu search indexes. Most of these articles were negative and expressed the public’s weak confidence to the China-made vaccines. Public confidence in vaccines was undermined by the actions of the manufacturer and the government. Conclusions The DTP vaccine crisis led to panic about immunization and eroded trust in the immunization program and in the government. Restoring public confidence in Chinese-made vaccines will take a long time, and meticulous management in vaccine production, and strict government regulation will help to alleviate public anxiety about vaccine safety and ultimately restore confidence.

The dynamical behaviors of an isolated population model involving delay-dependent parameters are investigated. It is shown that the positive equilibrium switches from being stable to unstable and then back to stable as the delay increases, and the Hopf bifurcation occurs finite times between the two critical values of stability changes which can be analytically determined. Moreover, the bifurcating periodic solutions are expressed analytically in an approximate form by the perturbation approach and Floquet technique. The direction and stability of the bifurcating periodic solutions are also determined. Finally, the validity of the results is shown by the consistency with the numerical simulations.

Details about the movement of trucks on postal express lines are investigated to improve the performances of mail distribution. A mixed driving pattern of trucks is introduced to minimize the transportation cost of a postal express line network with a service level requirement. We formulate this problem as a mixed p meeting depots location with shipment scheduling problem and build a MINLP model. A two-level tabu search procedure based on shipment grouping method is developed. Through a series of computational experiments and sensitivity analysis on different instances, some managerial insights of the network under mixed driving pattern are revealed.

In this paper, we characterize the humanitarian objectives of emergency resource allocation and distribution in disaster response operations. We formulate the humanitarian principles as three objective functions, i.e., lifesaving utility, delay cost and fairness. An integrated multi-objective optimization model that combines resource allocation with emergency distribution is developed, where a time space network is used to incorporate the frequent information and decision updates in a rolling horizon approach. The proposed model is shown to be a convex quadratic network flow problem, for which we design an efficient Variational Inequality algorithm. Computational results are reported to illustrate the performance of the proposed model and algorithm.

This paper examines contingent rerouting strategy for enhancing supply chain resilience taking a supplier's point of view. We consider a supply chain with multiple suppliers at each stage and establish a mathematical model for product allocation behavior among different suppliers. The allocation model is based on each supplier's production capacity, product quality, production cost, as well as possible decision maker's preferences. As a performance measure for rerouting strategy, we use the total outflow of the supply chain. We propose an optimization model and its solution determines the rerouting strategy for product flow through the supply chain under disruptions. Numerical examples demonstrate the effect of the rerouting strategy and show the resilience of the supply chain.

Disasters always cause tremendous injuries and medical resources demands in a short period, while improving resilience of hospital network can help to quickly recover from disruptions. Based on biological cell elasticity theory, this paper assimilates the hospital network to the cellular tissue. And we define an indicator of multi-hospitals network resilience that quantifies the ability of hospital network to react to the medical resources demand disruptions due to natural or human-caused disaster. And a collaborative scheduling model of resources in the network is put forward as the reactive strategy to improve the resilience of the hospitals network against disasters. Finally, numerical examples are given to illustrate the resilience concept and assess the effectiveness of the collaborative scheduling.

Aggregate production planning for highly re--entrant production processes is typically generated by finding optimal release rates based on clearing function models. For production processes with very long cycle times, like in semiconductor production, dispatch policies are used to cover short term fluctuations. We extend the concept of a clearing function to allow control over both, the release rates and priority allocations in re-entrant production. This approach is used to improve the production planning problem using combined release and the allocation dispatch policy. The control parameter for priority allocation, called the push-pull point (PPP), separates the beginning of the factory which employs a push policy from the end of the factory, which uses a pull policy. The extended clearing function model describes the output of the factory as a function of the work in progress (wip) and the position of the PPP. The model's qualitative behavior is analyzed. Numerical optimization results are compared to production planning based only on releases. It is found that controlling the PPP significantly reduces the average wip in the system and hence leads to much shorter cycle times.

In view of a fact that compact manifolds have the important application in practice, this paper provides a local consensus protocol for connected compact Riemannian manifolds, where communication network can be directed and dynamically switching. Meanwhile, with the help of the compression-decompression functions, we extend the local protocol and propose a global consensus algorithm for such manifolds. Furthermore, to demonstrate their effectiveness and efficiency, the global algorithm is applied to the consensus problem of rotation attitudes, together with a case simulation.

Our research is aimed at developing a quantitative approach for assessing predicted supply chain resilience based on structural evolution against random supply disruptions. The paper begins by iden...

In this paper, we concentrate on solving a convex time space network flow problem with decomposable structures. We first describe the convex time space network flow optimization model, and transform it into an equivalent variational inequality problem. Then, after exploring the decomposable structure of primal decision variables, we propose a novel decomposable self-adaptive projection-based prediction-correction algorithm (DSPPCA) to solve the model, and then further provide its convergent theory. Finally, we report the computational performances through computational experiments. Numerical results reveal that DSPPCA not only can enhance the accuracy and convergence rate significantly, but also can be a powerful search algorithm for convex optimization problems with decomposable structures of decision variables.

In this paper, we show that a state feedback method, which has successfully been used to control unstable steady states or periodic orbits, provides a tool to control the Hopf bifurcation for a novel congestion control model, i.e., the exponential RED algorithm with a single link and single source. We choose the gain parameter as the bifurcation parameter. Without control, the bifurcation will occur early; meanwhile, the model can maintain a stationary sending rate only in a certain domain of the gain parameter. However, outside of this domain the model still possesses a stable sending rate that can be guaranteed by the state feedback control, and the onset of the undesirable Hopf bifurcation is postponed. Numerical simulations are given to justify the validity of the state feedback controller in the bifurcation control.

It is known that a consensus problem on any connected complete Riemannian manifold can be transformed into the one on its strongly convex balls via the compression–decompression along geodesics. From the viewpoint of interior metrics, this paper mainly provides a consensus protocol for strongly convex geodesic balls, in which the communication can be switching and directed. With the aid of nonsmooth analysis tools on Riemannian manifolds, our analysis shows that all dynamical points involved can achieve consensus in finite time. Meanwhile, the corresponding global algorithm is given, with its application to the consensus problem of rotation attitudes, as well as a case simulation, to demonstrate and verify our proposed techniques. Refereed/Peer-reviewed

Numerous low-resolution (LR) face images are captured by a growing number of surveillance cameras nowadays. In some particular applications, such as suspect identification, it is required to recognize an LR face image captured by the surveillance camera using only one high-resolution (HR) profile face image on the ID card. This leads to LR face recognition with single sample per person (SSPP), which is more challenging than conventional LR face recognition or SSPP face recognition. To address this tough problem, we propose a Boosted Coupled Marginal Fisher Analysis (CMFA) approach, which unites domain adaptation and coupled mappings. An auxiliary database containing multiple HR and LR samples is introduced to explore more discriminative information, and locality preserving domain adaption (LPDA) is designed to realize good domain adaptation between SSPP training set (target domain) and auxiliary database (source domain). We perform LPDA on HR and LR images in both domains, then in the domain adaptation space we apply CMFA to learn the discriminative coupled mappings for classification. The learned coupled mappings embed knowledge from the auxiliary dataset, thus their discriminative ability is superior. We extensively evaluate the proposed method on FERET, LFW and SCface database, the promising results demonstrate its effectiveness on LR face recognition with SSPP.

Iris biometry has been widely used to recognize an individual in a natural and intuitive way. Conventional iris recognition systems transfer iris images to rectangle using polar coordinate after accurate segmentation, and have performed very well on accurate data. However, bovine iris images are usually irregular with respect to inactive participant, and the conventional methods cannot achieve high accuracy and true rotation invariance. In this paper, a new scheme is proposed based on scale invariant feature transform (SIFT) and bag-of-features. Firstly, region-based active contour is used to detect the inner boundary. Secondly, SIFT method is applied to detect the keypoints in the iris image, and points located in pupil region are removed. Then, feature vocabulary is constructed, and histogram representation for each iris image is generated. Finally, histogram distance is adopted for the matching test. Experimental results are provided to show the effectiveness and potential of developed noncooperative iris recognition.

In view of the fact that prototypes of Riemannian manifolds range from Euclidean surfaces to Lie groups, operator spaces, etc., the paper explores common consensus schemes for connected complete Riemannian manifolds. After studying the restriction exponential map, we introduce a local consensus protocol with switching communication topologies for the manifolds via the isometric mapping, based on one of the Euclidean consensus protocols. Equipped with the compression–decompression functions, we extend the local consensus protocol, and put forth a global consensus algorithm for any such manifolds. These consensus schemes can provide common solutions to the consensus problems arising from various nonlinear spaces or abstract spaces belonging to connected complete Riemannian manifolds. In particular, they are simple and concise, and can converge in finite time. Simulation on the unit sphere is provided to verify our consensus schemes, and to demonstrate the usage of our proposed techniques.

Demand for emergency resources is usually uncertain and varies quickly in anti-bioterrorism system. Besides, emergency resources which had been allocated to the epidemic areas in the early rescue cycle will affect the demand later. In this article, an integrated and dynamic optimisation model with time-varying demand based on the epidemic diffusion rule is constructed. The heuristic algorithm coupled with the MATLAB mathematical programming solver is adopted to solve the optimisation model. In what follows, the application of the optimisation model as well as a short sensitivity analysis of the key parameters in the time-varying demand forecast model is presented. The results show that both the model and the solution algorithm are useful in practice, and both objectives of inventory level and emergency rescue cost can be controlled effectively. Thus, it can provide some guidelines for decision makers when coping with emergency rescue problem with uncertain demand, and offers an excellent reference when issues pertain to bioterrorism.